Project Summary Vector-borne diseases (VBDs) are the most common types of emerging and re-emerging infectious diseases in the world. VBD epidemics have been increasing over recent decades, with tickborne diseases having doubled in the last decade in the United States. Despite the increase in public health burden, over 80% of vector-control organizations lack preventative capabilities. Understanding the interplay between the environment, vectors, pathogens, and humans that expedite disease spread remains a challenge. The overarching goal of this project is to identify the key environmental and human drivers that have led to the emergence of VBDs. Current models that predict tickborne disease risk have oversimplified the process by focusing only on the vector, i.e. risk of tick exposure. A human?s risk of infection is not only a function of entomological risk but also of factors inherent to the individual including behavior or characteristics that increase susceptibility to disease. This project proposes a novel approach to tickborne disease prediction by developing a comprehensive model that incorporate pathogen population dynamics and human factors to predict disease risk. This study will investigate several pathogens vectored by the black-legged tick (Ixodes scapularis): Borrelia burgdorferi (Lyme disease), Anaplasma phagocytophilum (human granulocytic anaplasmosis), and Babesia microti (babesiosis). The central hypothesis is that the prediction of tickborne disease risk can be improved by using sophisticated statistical methods to identify environmental drivers that impact pathogen population dynamics while incorporating human demographic characteristics. The hypothesis will be addressed in the following aims: (1) Determine the current and historical population dynamic patterns of pathogens vectored by I. scapularis to predict pathogen distribution; (2) Determine the association between human characteristics and tick-borne disease risk in order to develop an improved spatial disease risk model. This model will allow the identification and quantification of factors that are associated with the emergence of tickborne diseases in New York State, which is geographically advantageous because it is representative of much of the natural environment that ticks encounter in the northeastern US including rapid and recent changes in climate and landscapes. The results of this project will be used to develop a public disease warning system that will use contemporary and future climate forecasts to monitor tick populations and predict potential disease outbreaks for areas with vulnerable populations. With climate forecasts predicting an increase in 2-3C in temperature by 2100, there is uncertainty in how diseases will shift and a warning system will allow preparation accordingly. At the completion of the proposed research project, the applicant will have acquired the following skillsets through intensive, interdisciplinary mentorship: big data analysis, advanced statistics including Bayesian and machine learning methods, spatial analyses, and risk analysis. This will enable the applicant to succeed as an independent investigator to address the challenges posed by emerging infectious diseases.